“Pose” is a term commonly used in unmanned vehicle navigation to reference a directional heading of the vehicle. Additionally, depending on context, the pose of the vehicle also can include its motion (e.g., velocity) and distance of travel. To this extent, a “pose estimator” refers to a system or method for determining how far an unmanned vehicle has traveled, the direction of travel, and the current directional heading of the vehicle.
Unmanned system navigation initially used a single method, such as wheel odometry, inertial tracking, optical flow, and/or the like. However, each individual method is subject to a certain degree of uncertainty. For example, the wheels of a vehicle can slip, causing them to register distance or turning when the vehicle has actually failed to move. A global positioning system (GPS) unit has randomly-varying uncertainty, which causes the determined location to fluctuate within a bounded area, the shape of which varies with location on the planetary surface. An inertial tracking system also is subject to noise, which can result in an illusion of movement when there is no movement or shift the detected motion in some unknown direction.
Previous approaches to reducing the uncertainty in each of these methods have been utilized. For example, Kalman filtering can be applied to successive readings. While such approaches can reduce the uncertainty to some extent, none of the approaches is able to produce sufficiently accurate results for numerous applications of unmanned vehicles. In particular, the GPS system tends to be extremely poor at providing heading/facing information and the inertial system and wheel odometry approaches tend to result in cumulative errors over time.
Current approaches frequently combine, or fuse, the results of two or more pose information acquisition methods to provide a more reliable overall pose estimation for an unmanned vehicle. For example, a current approach includes adding a simple refinement, such as adding a compass to provide a more objective baseline for facing information. Another approach includes fusing multiple pose estimation methods, each of which individually provides its own full pose estimate. To date, these approaches assume that, as each data acquisition method is generally independent, the source(s) of the corresponding noise or uncertainty in each data acquisition method are independently random. As a result, current approaches assume that combining the results of multiple data acquisition approaches will allow the combination to significantly reduce the overall uncertainty in the resulting pose estimation.